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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Hoang-Sy Nguyen
Eastern International University
Computer Science & IT

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Overall outage event of self-sustaining low-power cooperative relaying networks Hoang-Sy Nguyen; Thanh-Khiet Bui
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.21925

Abstract

This paper investigates the implementation of the household low-power energy harvesting (LoPEH) wireless sensor networks (WSN) over log-normal fading channels. The relays are battery-operated and the stochastic harvested energy flow to the batteries is characterized with the Markov property of energy buffer status. The communication is established with the combination of direct link and cooperative relays. The best relay is chosen based on a relay selection (RS) scheme namely optimal relay selection (OPRS). It can be drawn that within a particular range of signal-to-noise ratio (SNR), the energy harvesting (EH) relay-aided protocol can remarkably boost the overall system performance. On the other hand, the study reports how increasing the log-normal channel variance can degenerate the in-studied EH relaying protocol.
Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model Hoang-Sy Nguyen; Thu Ngan Phan Thi; Cong-Danh Huynh
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp567-576

Abstract

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19.
Co-Authors Cong-Danh Huynh Thanh-Khiet Bui Thu Ngan Phan Thi